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Load forecasting is a data intensive statistical method. Internet of things (IoT) based online load forecasting (LF) collects those data from internet on demand and then performs fast statistical and optimization methods for forecasting efficiently. IoT based online LF not only depends on power systems properties, but also internet, machine-to-machine (M2M) connections, communications and computation...
This paper introduces the multiple linear regression, stepwise linear regression, neural network method, and improves the neural network. Comprehensive analysis of the current prediction methods, the application principle of a detailed analysis and comparison of the various prediction methods advantages and disadvantages. Put forward to improve short-term load forecasting accuracy is not only attach...
In order to avoid some phenomena such as too many regulating times of on load tap changer, too frequent actions of capacitor switching, lower bus voltage qualification rate and higher network loss, it is presented that a control strategy for optimization of voltage and reactive power in substation based on load forecasting in this paper. The load and system voltage forecasting are realized by using...
Microgrids are a rapidly growing sector of smart grids, which will be an essential component in the trend toward distributed electricity generation. In the operation of a microgrid, forecasting the short-term load is an important task. With a more accurate short-term loaf forecast (STLF), the microgrid can enhance the management of its renewable and conventional resources and improve the economics...
This paper presents the development of an Artificial Neural Networks and Particle Swarm Optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land...
The paper presents an improved method for a 24-hour load forecasting in the power system by using self organizing map (SOM) of Kohonen neural network .In this paper two models are compared with each other. The main difference between these models is about determining the training patterns procedure. In the first basic model, the training of neural networks performs in similar patterns with the most...
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